The objective is to train a neural network to be able to identify and automatically classify plasma wave emissions in planetary (and terrestrial) spacecraft in situ data. In particular, identification of whistler mode emissions such as chorus, lion roars and solitary waves is valuable and feasible due to existing human classified datasets. Data from Earth's (Cluster, THEMIS, Van Allen Probes) and outer planet missions (Cassini and Juno) can be used for training.

The main aim is to successfully identify a particular type of wave emission and its classification. Further, autonomous extraction of wave properties (polarization, propagation, spectral shape) is desirable.